Multi-scale coarse-graining of non-conservative interactions in molecular liquids.

A new bottom-up procedure for constructing non-conservative (dissipative and stochastic) interactions for dissipative particle dynamics (DPD) models is described and applied to perform hierarchical coarse-graining of a polar molecular liquid (nitromethane). The distant-dependent radial and shear frictions in functional-free form are derived consistently with a chosen form for conservative interactions by matching two-body force-velocity and three-body velocity-velocity correlations along the microscopic trajectories of the centroids of Voronoi cells (clusters), which represent the dissipative particles within the DPD description. The Voronoi tessellation is achieved by application of the K-means clustering algorithm at regular time intervals. Consistently with a notion of many-body DPD, the conservative interactions are determined through the multi-scale coarse-graining (MS-CG) method, which naturally implements a pairwise decomposition of the microscopic free energy. A hierarchy of MS-CG/DPD models starting with one molecule per Voronoi cell and up to 64 molecules per cell is derived. The radial contribution to the friction appears to be dominant for all models. As the Voronoi cell sizes increase, the dissipative forces rapidly become confined to the first coordination shell. For Voronoi cells of two and more molecules the time dependence of the velocity autocorrelation function becomes monotonic and well reproduced by the respective MS-CG/DPD models. A comparative analysis of force and velocity correlations in the atomistic and CG ensembles indicates Markovian behavior with as low as two molecules per dissipative particle. The models with one and two molecules per Voronoi cell yield transport properties (diffusion and shear viscosity) that are in good agreement with the atomistic data. The coarser models produce slower dynamics that can be appreciably attributed to unaccounted dissipation introduced by regular Voronoi re-partitioning as well as by larger numerical errors in mapping out the dissipative forces. The framework presented herein can be used to develop computational models of real liquids which are capable of bridging the atomistic and mesoscopic scales.

[1]  P. M. Rodger,et al.  DL_POLY: Application to molecular simulation , 2002 .

[2]  Gregory A Voth,et al.  Efficient, Regularized, and Scalable Algorithms for Multiscale Coarse-Graining. , 2010, Journal of chemical theory and computation.

[3]  George Em Karniadakis,et al.  Direct construction of mesoscopic models from microscopic simulations. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Eric Darve,et al.  Computing generalized Langevin equations and generalized Fokker–Planck equations , 2009, Proceedings of the National Academy of Sciences.

[5]  Qiang Du,et al.  Centroidal Voronoi Tessellations: Applications and Algorithms , 1999, SIAM Rev..

[6]  M Scott Shell,et al.  The relative entropy is fundamental to multiscale and inverse thermodynamic problems. , 2008, The Journal of chemical physics.

[7]  W. Schommers A pair potential for liquid rubidium from the pair correlation function , 1973 .

[8]  P. Español,et al.  FLUID PARTICLE MODEL , 1998 .

[9]  P. Español Hybrid description of complex molecules , 2009 .

[10]  Valentina Tozzini,et al.  Coarse-grained models for proteins. , 2005, Current opinion in structural biology.

[11]  J. Andrew McCammon,et al.  Generalized Langevin dynamics simulations with arbitrary time‐dependent memory kernels , 1983 .

[12]  Sergei Izvekov,et al.  Microscopic derivation of particle-based coarse-grained dynamics. , 2013, The Journal of chemical physics.

[13]  P. Español,et al.  Force autocorrelation function in Brownian motion theory , 1993 .

[14]  Sauro Succi,et al.  Three Routes to the Friction Matrix and Their Application to the Coarse‐Graining of Atomic Lattices , 2011 .

[15]  Alexandre J. Chorin,et al.  Optimal prediction with memory , 2002 .

[16]  P. Español,et al.  Statistical Mechanics of Dissipative Particle Dynamics. , 1995 .

[17]  R. D. Groot Electrostatic interactions in dissipative particle dynamics—simulation of polyelectrolytes and anionic surfactants , 2003 .

[18]  Berend Smit,et al.  Investigation of Surfactant Efficiency Using Dissipative Particle Dynamics , 2003 .

[19]  P. B. Warren Vapor-liquid coexistence in many-body dissipative particle dynamics. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Betsy M. Rice,et al.  Molecular Dynamics Simulations of Normal Mode Vibrational Energy Transfer in Liquid Nitromethane , 2004 .

[21]  H. Mori Transport, Collective Motion, and Brownian Motion , 1965 .

[22]  Gregory A. Voth,et al.  Coarse-graining away electronic structure: a rigorous route to accurate condensed phase interaction potentials , 2012 .

[23]  Formal derivation of dissipative particle dynamics from first principles. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  K. Tunstrøm,et al.  Bottom-up derivation of an effective thermostat for united atoms simulations of water. , 2009, The Journal of chemical physics.

[25]  Maj Thijs Michels,et al.  Thermodynamic consistency in dissipative particle dynamics simulations of strongly nonideal liquids and liquid mixtures , 2002 .

[26]  B. Rice,et al.  Free-energy based pair-additive potentials for bulk Ni-Al systems: application to study Ni-Al reactive alloying. , 2012, The Journal of chemical physics.

[27]  K. Tunstrøm,et al.  Using force covariance to derive effective stochastic interactions in dissipative particle dynamics. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  D. Talaga,et al.  DPD Simulation of Protein Conformations: From α-Helices to β-Structures. , 2012, The journal of physical chemistry letters.

[29]  Michael L Klein,et al.  Self-assembly and properties of diblock copolymers by coarse-grain molecular dynamics , 2004, Nature materials.

[30]  Thermodynamically Admissible Form for Discrete Hydrodynamics , 1999, cond-mat/9901101.

[31]  Shi-aki Hyodo,et al.  Equation of motion for coarse-grained simulation based on microscopic description. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[32]  A J Chorin,et al.  Optimal prediction and the Mori-Zwanzig representation of irreversible processes. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[33]  Gregory A Voth,et al.  Modeling real dynamics in the coarse-grained representation of condensed phase systems. , 2006, The Journal of chemical physics.

[34]  Matej Praprotnik,et al.  Transport properties controlled by a thermostat: An extended dissipative particle dynamics thermostat. , 2007, Soft matter.

[35]  J. Koelman,et al.  Simulating microscopic hydrodynamic phenomena with dissipative particle dynamics , 1992 .

[36]  B. Rice,et al.  Mechanism of densification in silica glass under pressure as revealed by a bottom-up pairwise effective interaction model. , 2012, The Journal of chemical physics.

[37]  Gregory A Voth,et al.  Multiscale coarse graining of liquid-state systems. , 2005, The Journal of chemical physics.

[38]  R. Füchslin,et al.  Coarse graining and scaling in dissipative particle dynamics. , 2007, The Journal of chemical physics.

[39]  Gregory A Voth,et al.  A multiscale coarse-graining method for biomolecular systems. , 2005, The journal of physical chemistry. B.

[40]  Gregory A Voth,et al.  The multiscale coarse-graining method. II. Numerical implementation for coarse-grained molecular models. , 2008, The Journal of chemical physics.

[41]  Jhih-Wei Chu,et al.  Emerging methods for multiscale simulation of biomolecular systems , 2007 .

[42]  Pep Español,et al.  Smoothed dissipative particle dynamics. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[43]  Florian Müller-Plathe,et al.  Coarse-graining in polymer simulation: from the atomistic to the mesoscopic scale and back. , 2002, Chemphyschem : a European journal of chemical physics and physical chemistry.

[44]  Tamás Grósz,et al.  Structure of liquid nitromethane: comparison of simulation and diffraction studies. , 2007, The Journal of chemical physics.

[45]  John K. Brennan,et al.  An enhanced entangled polymer model for dissipative particle dynamics. , 2012, The Journal of chemical physics.

[46]  Sergei Izvekov,et al.  The multiscale coarse-graining method: assessing its accuracy and introducing density dependent coarse-grain potentials. , 2010, The Journal of chemical physics.

[47]  Transport coefficients of non-Newtonian fluid and causal dissipative hydrodynamics. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[48]  Pep Español,et al.  Markovian approximation in a coarse-grained description of atomic systems. , 2006, The Journal of chemical physics.

[49]  E. Forgy,et al.  Cluster analysis of multivariate data : efficiency versus interpretability of classifications , 1965 .

[50]  Weihai Fang,et al.  Semi-bottom-up coarse graining of water based on microscopic simulations. , 2011, The Journal of chemical physics.

[51]  N. A. Spenley Scaling laws for polymers in dissipative particle dynamics , 2000 .

[52]  Peter V. Coveney,et al.  From Molecular Dynamics to Dissipative Particle Dynamics , 1999 .

[53]  W Smith,et al.  DL_POLY_2.0: a general-purpose parallel molecular dynamics simulation package. , 1996, Journal of molecular graphics.

[54]  P. B. Warren,et al.  DISSIPATIVE PARTICLE DYNAMICS : BRIDGING THE GAP BETWEEN ATOMISTIC AND MESOSCOPIC SIMULATION , 1997 .

[55]  G. Karniadakis,et al.  Coarse-graining limits in open and wall-bounded dissipative particle dynamics systems. , 2006, The Journal of chemical physics.

[56]  B. Rice,et al.  Theoretical Studies of Solid Nitromethane , 2000 .

[57]  J. Swanson,et al.  Using force-matching to reveal essential differences between density functionals in ab initio molecular dynamics simulations. , 2011, The Journal of chemical physics.

[58]  M. Moreno,et al.  Computational Experiments on Filled Rubber Viscoelasticity: What Is the Role of Particle−Particle Interactions? , 2006 .

[59]  Gregory A. Voth,et al.  The multiscale coarse-graining method. I. A rigorous bridge between atomistic and coarse-grained models. , 2008, The Journal of chemical physics.

[60]  A. Striolo,et al.  Mechanistic study of droplets coalescence in Pickering emulsions , 2012 .

[61]  J. Ávalos,et al.  Dissipative particle dynamics with energy conservation , 1997, cond-mat/9706217.

[62]  Sergei Izvekov,et al.  Towards an understanding of many-particle effects in hydrophobic association in methane solutions. , 2011, The Journal of chemical physics.

[63]  Dirk Reith,et al.  Deriving effective mesoscale potentials from atomistic simulations , 2002, J. Comput. Chem..

[64]  A. Louis Beware of density dependent pair potentials , 2002, cond-mat/0205110.

[65]  David A. Yuen,et al.  Matching macroscopic properties of binary fluids to the interactions of dissipative particle dynamics , 2000 .

[66]  Peter V. Coveney,et al.  Simulating the rheology of dense colloidal suspensions using dissipative particle dynamics , 1997 .

[67]  Sauro Succi,et al.  Bottom-up coarse-graining of a simple graphene model: the blob picture. , 2011, The Journal of chemical physics.

[68]  Gregory A. Voth,et al.  The Multiscale Coarse- Graining Method: A Systematic Approach to Coarse-Graining , 2008 .

[69]  R. Zwanzig Ensemble Method in the Theory of Irreversibility , 1960 .

[70]  I. Pagonabarraga,et al.  Dissipative particle dynamics for interacting systems , 2001, cond-mat/0105075.

[71]  A. Lyubartsev,et al.  Calculation of effective interaction potentials from radial distribution functions: A reverse Monte Carlo approach. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[72]  Flekkoy,et al.  Foundations of dissipative particle dynamics , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[73]  B. Rice,et al.  Particle-based multiscale coarse graining with density-dependent potentials: application to molecular crystals (hexahydro-1,3,5-trinitro-s-triazine). , 2011, The Journal of chemical physics.

[74]  D. N. Sparks Euclidean Cluster Analysis , 1973 .

[75]  Ignacio Pagonabarraga,et al.  Self-consistent dissipative particle dynamics algorithm , 1998 .

[76]  D. Tieleman,et al.  The MARTINI force field: coarse grained model for biomolecular simulations. , 2007, The journal of physical chemistry. B.

[77]  B. Berne,et al.  Non‐Markovian activated rate processes: Comparison of current theories with numerical simulation data , 1986 .

[78]  Michael L. Klein,et al.  Coarse grain models and the computer simulation of soft materials , 2004 .

[79]  Yijian Chen,et al.  Mesoscopic simulation of the aggregation behavior of fluorinated surfactant in aqueous solution , 2006 .

[80]  M Scott Shell,et al.  Systematic coarse-graining of potential energy landscapes and dynamics in liquids. , 2012, The Journal of chemical physics.

[81]  A. Ghoufi,et al.  Calculation of the surface tension from multibody dissipative particle dynamics and Monte Carlo methods. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[82]  Reinier L C Akkermans,et al.  Coarse-grained dynamics of one chain in a polymer melt , 2000 .

[83]  Gregory A Voth,et al.  Multiscale coarse-graining of ionic liquids. , 2006, The journal of physical chemistry. B.

[84]  E. Vanden-Eijnden,et al.  Mori-Zwanzig formalism as a practical computational tool. , 2010, Faraday discussions.

[85]  Eugenio Oñate,et al.  Particle-Based Methods , 2011 .

[86]  Betsy M. Rice,et al.  Molecular Dynamics Simulations of Liquid Nitromethane , 2001 .

[87]  E. Lomba,et al.  Determination of the interaction potential from the pair distribution function: an inverse Monte Carlo technique. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[88]  Betsy M. Rice,et al.  Molecular dynamics study of the melting of nitromethane , 2003 .

[89]  Bernard Rousseau,et al.  Influence of the adjustable parameters of the DPD on the global and local dynamics of a polymer melt , 2007 .

[90]  Wataru Shinoda,et al.  Computer simulation studies of self-assembling macromolecules. , 2012, Current opinion in structural biology.

[91]  R. D. Groot,et al.  Mesoscopic simulation of cell membrane damage, morphology change and rupture by nonionic surfactants. , 2001, Biophysical journal.

[92]  W. Dzwinel,et al.  Using Discrete Particles As a Natural Solver In Simulating Multiple-Scale Phenomena , 2000 .

[93]  K. Tunstrøm,et al.  A method for estimating the interactions in dissipative particle dynamics from particle trajectories , 2008, Journal of physics. Condensed matter : an Institute of Physics journal.